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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 9170

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Dr. Weicun Zhang

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Guest Editor

School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: adaptive control; self-tuning control; multiple model adaptive control; multiple model adaptive estimation; stability analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Quanmin Zhu

E-Mail Website
Guest Editor

School of Engineering, University of the West of England-Frenchy Campus, Coldharbour Lane, Bristol, UK
Interests: dynamic system modeling; identification; control; simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Adaptive control originated from the gain scheduling control of high-performance aircraft in the early 1950s. To be specific, model reference adaptive control (MRAC) was proposed by Whitaker et al. to solve the control problem of an autopilot. From the viewpoint of theory research, self-tuning control (STC) was proposed by Kalman in 1958 to deal with the optimal control of a stochastic system with unknown or time-varying parameters and then connected with actual applications in paper-making machine through the pioneering work of Astrom and Wittenmark. It is well known that identification is the most important component of an adaptive control system.

This Special Issue will explore recent technological developments in adaptive identification and control (design methods and theoretical analysis), especially for nonlinear stochastic processes such as robotic systems, manufacturing systems, transportation systems, power systems, chemical systems, etc.

Original research articles and reviews are welcome in this Special Issue. Research areas may include (but are not limited to) the following:

Identification and self-tuning adaptive control;
Event-triggered adaptive identification and control;
Intelligent adaptive control;
Robust adaptive control;
Adaptive sliding-mode control.

Dr. Weicun Zhang
Prof. Dr. Quanmin Zhu
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

adaptive identification and control systems
design and analysis
adaptive identification and control system simulation
process modeling/identification
applications of adaptive identification and control system
stability and convergence of adaptive identification and control

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Published Papers (11 papers)

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Research

34 pages, 14222 KB

Open AccessArticle

Linear Algebra-Based Internal Model Control Strategies for Non-Minimum Phase Systems: Design and Evaluation

by Sebastián Insuasti, Gabriel Gómez-Guerra, Gustavo Scaglia and Oscar Camacho

Processes 2025, 13(9), 2942; https://doi.org/10.3390/pr13092942 - 15 Sep 2025

Viewed by 90

Abstract

This paper addresses the challenge of trajectory tracking in non-minimum-phase systems, which are known for their limitations in performance and stability within process control. The primary objective is to evaluate the feasibility of using linear-algebra-based control strategies to achieve precise tracking in such systems. The primary hypothesis is that internal model-based compensators can transform non-minimum-phase behavior into equivalent minimum-phase dynamics, thereby enabling the application of linear algebra techniques for controller design. To validate this approach, both simulation and experimental tests are conducted, first with a Continuous Stirred Tank Reactor (CSTR) model and then with the TCLab educational platform. The results show that the proposed method effectively achieves robust trajectory tracking, even in the presence of external disturbances and sensor noise. The primary contribution of this work is to demonstrate that internal model-based compensation enables the application of linear control methods to a class of systems that are typically considered challenging to control. This not only simplifies the design process but also enhances control performance, highlighting the practical relevance and applicability of the approach for real-world non-minimum-phase systems processes. Full article

(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)

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16 pages, 1362 KB

Open AccessArticle

A Robust Fuzzy Adaptive Control Scheme for PMSM with Sliding Mode Dynamics

by Guangyu Cao, Zhihan Chen, Daoyuan Wang, Xiujing Zhao and Fanwei Meng

Processes 2025, 13(8), 2635; https://doi.org/10.3390/pr13082635 - 20 Aug 2025

Viewed by 381

Abstract

A key trade-off persists in the control of permanent magnet synchronous motors (PMSMs): achieving fast finite-time convergence often exacerbates control chattering, while conventional chattering-suppression methods can compromise the system’s dynamic response. The existing literature often addresses these challenges in isolation. The core original contribution of this research lies in proposing a novel robust fuzzy adaptive control scheme that effectively resolves this trade-off through a synergistic design. The contributions are as follows: (1) A novel reaching law is formulated to significantly accelerate error convergence, achieving finite-time stability and improving upon conventional reaching law designs. (2) A super-twisting sliding mode observer is integrated into the control loop, providing accurate real-time estimation of load torque disturbances, which is used for feedforward compensation to drastically improve the system’s disturbance rejection capability. (3) A fuzzy adaptive mechanism is developed to dynamically tune key gains in the sliding mode law. This approach effectively suppresses chattering without sacrificing response speed, enhancing system robustness. (4) The stability and convergence of the proposed controller are rigorously analyzed. Simulations, comparing the proposed method with conventional adaptive sliding mode control (ASMC), demonstrate its marked superiority in control accuracy, transient behavior, and disturbance rejection. This work provides an integrated solution that balances rapidity and smoothness for high-performance motor control, offering significant theoretical and engineering value. Full article

(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)

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25 pages, 2473 KB

Open AccessArticle

Predefined-Time Adaptive Neural Control with Event-Triggering for Robust Trajectory Tracking of Underactuated Marine Vessels

by Hui An, Zhanyang Yu, Jianhua Zhang, Xinxin Wang and Cheng Siong Chin

Processes 2025, 13(8), 2443; https://doi.org/10.3390/pr13082443 - 1 Aug 2025

Viewed by 357

Abstract

This paper addresses the trajectory tracking control problem of underactuated ships in ocean engineering, which faces the dual challenges of tracking error time–performance regulation and robustness design due to the system’s underactuated characteristics, model uncertainties, and external disturbances. Aiming to address the issues of traditional finite-time control (convergence time dependent on initial states) and fixed-time control (control chattering and parameter conservativeness), this paper proposes a predefined-time adaptive control framework that integrates an event-triggered mechanism and neural networks. By constructing a Lyapunov function with time-varying weights and designing non-periodic dynamically updated dual triggering conditions, the convergence process of tracking errors is strictly constrained within a user-prespecified time window without relying on initial states or introducing non-smooth terms. An adaptive approximator based on radial basis function neural networks (RBF-NNs) is employed to compensate for unknown nonlinear dynamics and external disturbances in real-time. Combined with the event-triggered mechanism, it dynamically adjusts the update instances of control inputs, ensuring prespecified tracking accuracy while significantly reducing computational resource consumption. Theoretical analysis shows that all signals in the closed-loop system are uniformly ultimately bounded, tracking errors converge to a neighborhood of the origin within the predefined-time, and the update frequency of control inputs exhibits a linear relationship with the predefined-time, avoiding Zeno behavior. Simulation results verify the effectiveness of the proposed method in complex marine environments. Compared with traditional control strategies, it achieves more accurate trajectory tracking, faster response, and a substantial reduction in control input update frequency, providing an efficient solution for the engineering implementation of embedded control systems in unmanned ships. Full article

(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)

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18 pages, 9954 KB

Open AccessArticle

Adaptive Continuous Non-Singular Terminal Sliding Mode Control for High-Pressure Common Rail Systems: Design and Experimental Validation

by Jie Zhang, Yinhui Yu, Sumin Wu, Wenjiang Zhu and Wenqian Liu

Processes 2025, 13(8), 2410; https://doi.org/10.3390/pr13082410 - 29 Jul 2025

Viewed by 388

Abstract

The High-Pressure Common Rail System (HPCRS) is designed based on fundamental hydrodynamic principles, after which this paper formally defines the key control challenges. The proposed continuous sliding mode control strategy is developed based on a non-singular terminal sliding mode framework, integrated with an improved power reaching law. This design effectively eliminates chattering and achieves fast dynamic response with enhanced tracking precision. Subsequently, a bidirectional adaptive mechanism is integrated into the proposed control scheme to eliminate the necessity for a priori knowledge of unknown disturbances within the HPCRS. This mechanism enables real-time evaluation of the system’s state relative to a predefined detection region. To validate the effectiveness of the proposed strategy, experimental studies are conducted under three distinct operating conditions. The experimental results indicate that, compared with conventional rail pressure controllers, the proposed method achieves superior tracking accuracy, faster dynamic response, and improved disturbance rejection. Full article

(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)

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23 pages, 3371 KB

Open AccessArticle

Scheduling Control Considering Model Inconsistency of Membrane-Wing Aircraft

by Yanxuan Wu, Yifan Fu, Zhengjie Wang, Yang Yu and Hao Li

Processes 2025, 13(8), 2367; https://doi.org/10.3390/pr13082367 - 25 Jul 2025

Viewed by 318

Abstract

Inconsistency in the structural strengths of a membrane wing under positive and negative loads has undesirable impacts on the aeroelastic deflections of the wing, which results in more significant flight control system modeling errors and worsens the performance of the aircraft. In this paper, an integrated dynamic model is derived for a membrane-wing aircraft based on the structural dynamics equation of the membrane wing and the flight dynamics equation of the traditional fixed wing. Based on state feedback control theory, an autopilot system is designed to unify the flight and control properties of different flight and wing deformation statuses. The system uses models of different operating regions to estimate the dynamic response of the vehicle and compares the estimation results with the sensor signals. Based on the compared results, the autopilot can identify the overall flight and select the correct operating region for the control system. By switching to the operating region with the minimum modeling error, the autopilot system maintains good flight performance while flying in turbulence. According to the simulation results, compared with traditional rigid aircraft autopilots, the proposed autopilot can reduce the absolute maximum attack angles by nearly 27% and the absolute maximum wingtip twist angles by nearly 25% under gust conditions. This enhanced robustness and stability performance demonstrates the autopilot’s significant potential for practical deployment in micro-aerial vehicles, particularly in applications demanding reliable operation under turbulent conditions, such as military surveillance, environmental monitoring, precision agriculture, or infrastructure inspection. Full article

(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)

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19 pages, 6245 KB

Open AccessArticle

Ensemble Learning-Based Approach for Forecasting Inventory Data in Prefabricated Component Warehousing

by Shuo Lin, Xianyu Huang, Shunchao Zhang and Zhonghua Han

Processes 2025, 13(5), 1443; https://doi.org/10.3390/pr13051443 - 8 May 2025

Cited by 1 | Viewed by 738

Abstract

Accurately predicting the storage area of prefabricated components facilitates transshipment scheduling and prevents the waste of storage space. Due to the influence of numerous factors, precise prediction remains challenging. Currently, limited research has addressed the prediction of storage areas for prefabricated components, and effective solutions are lacking. To address this issue, a GRU model with an attention mechanism based on ensemble learning was proposed. The model employed the Bo-Bi-ATT-GRU approach to address the time series prediction of storage areas. A Bayesian optimization algorithm was utilized to enhance parameter tuning and training efficiency, while an ensemble learning framework improved model stability. In this study, a port container dataset was used for experimentation, with root mean square error (RMSE) and mean absolute percentage error (MAPE) as evaluation metrics. Compared with the GM model, the R² of the proposed model improved by 3.38%. Experimental results demonstrated that the ensemble learning-based prediction model offered superior performance in forecasting the storage area of prefabricated components. Full article

(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)

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18 pages, 6607 KB

Open AccessArticle

Total Model-Free Robust Control of Non-Affine Nonlinear Systems with Discontinuous Inputs

by Quanmin Zhu, Jing Na, Weicun Zhang and Qiang Chen

Processes 2025, 13(5), 1315; https://doi.org/10.3390/pr13051315 - 25 Apr 2025

Cited by 2 | Viewed by 523

Abstract

Taking the plant as a total uncertainty in a black box with measurable inputs and attainable outputs, this paper presents a constructive control design of agnostic nonlinear dynamic systems with discontinuous input (such as hard nonlinearities in the forms of dead zones, friction, and backlashes). This study expands the model-free sliding mode control (MFSMC), based on the Lyapunov differential inequality, to a total model-free robust control (TMFRC) for this class of piecewise systems, which does not use extra adaptive online data fitting modelling to deal with plant uncertainties and input discontinuities. The associated properties are analysed to justify the constraints and provide assurance for system stability analysis. Numerical examples in control of a non-affine nonlinear plant with three hard nonlinear inputs—a dead zone, Coulomb and viscous friction, and backlash—are used to test the feasibility of the TMFRC. Furthermore, real experimental tests on a permanent magnet synchronous motor (PMSM) are also given to showcase the control’s applicability and offer guidance for implementation. Full article

(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)

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14 pages, 2536 KB

Open AccessArticle

Optimization of Weighted Geometrical Center Method for PI and PI-PD Controllers

by Mahmut Daskin

Processes 2025, 13(3), 749; https://doi.org/10.3390/pr13030749 - 4 Mar 2025

Viewed by 915

Abstract

This study proposes an optimized approach to enhance the performance of the Weighted Geometric Center (WGC) method for stabilizing time-delay systems, which has applications in industrial process control, robotics, and high-order dynamic systems. The traditional WGC method determines controller parameters by calculating the Weighted Geometric Center of the stable region, but it often overlooks better-performing parameter pairs near the WGC point. To address this limitation, a goal function is formulated based on percentage overshoot, rise time, and settling time. The optimization process explores the vicinity of the WGC and selects controller parameters that minimize the goal function, ensuring improved performance. The proposed optimization is applied to PI and PI-PD controllers, and its effectiveness is demonstrated through multiple case studies. Simulation results indicate that the optimized method significantly improves control performance, particularly in reducing overshoot, enhancing settling time, and ensuring a more stable response compared to the conventional WGC method. For instance, the Optimized WGC method reduces overshoot by up to 15% and settling time by up to 20%. These findings highlight the practical benefits of integrating local optimization into the WGC framework for superior controller tuning in time-delay systems. Full article

(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)

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27 pages, 5279 KB

Open AccessArticle

Research on Unmanned Aerial Vehicle Intelligent Maneuvering Method Based on Hierarchical Proximal Policy Optimization

by Yao Wang, Yi Jiang, Huiqi Xu, Chuanliang Xiao and Ke Zhao

Processes 2025, 13(2), 357; https://doi.org/10.3390/pr13020357 - 27 Jan 2025

Cited by 1 | Viewed by 1202

Abstract

Improving decision-making in the autonomous maneuvering of unmanned aerial vehicles (UAVs) is of great significance to improving flight safety, the mission execution rate, and environmental adaptability. The method of deep reinforcement learning makes the autonomous maneuvering decision of UAVs possible. However, the current algorithm is prone to low training efficiency and poor performance when dealing with complex continuous maneuvering problems. In order to further improve the autonomous maneuvering level of UAVs and explore safe and efficient maneuvering methods in complex environments, a maneuvering decision-making method based on hierarchical reinforcement learning and Proximal Policy Optimization (PPO) is proposed in this paper. By introducing the idea of hierarchical reinforcement learning into the PPO algorithm, the complex problem of UAV maneuvering and obstacle avoidance is separated into high-level macro-maneuver guidance and low-level micro-action execution, greatly simplifying the task of addressing complex maneuvering decisions using a single-layer PPO. In addition, by designing static/dynamic threat zones and varying their quantity, size, and location, the complexity of the environment is enhanced, thereby improving the algorithm’s adaptability and robustness to different conditions. The experimental results indicate that when the number of threat targets is five, the success rate of the H-PPO algorithm for maneuvering to the designated target point is 80%, which is significantly higher than the 58% rate achieved by the original PPO algorithm. Additionally, both the average maneuvering distance and time are lower than those of the PPO, and the network computation time is only 1.64 s, which is shorter than the 2.46 s computation time of the PPO. Additionally, as the complexity of the environment increases, the H-PPO algorithm outperforms other compared networks, demonstrating the effectiveness of the algorithm constructed in this paper for guiding intelligent agents to autonomously maneuver and avoid obstacles in complex and time-varying environments. This provides a feasible technical approach and theoretical support for realizing autonomous maneuvering decisions in UAVs. Full article

(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)

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21 pages, 28395 KB

Open AccessArticle

Sensorless Position Control in High-Speed Domain of PMSM Based on Improved Adaptive Sliding Mode Observer

by Liangtong Shi, Minghao Lv and Pengwei Li

Processes 2024, 12(11), 2581; https://doi.org/10.3390/pr12112581 - 18 Nov 2024

Cited by 1 | Viewed by 2158

Abstract

To improve the speed buffering and position tracking accuracy of medium–high-speed permanent magnet synchronous motor (PMSM), a sensorless control method based on an improved sliding mode observer is proposed. By the mathematical model of the built-in PMSM, an improved adaptive super-twisting sliding mode observer is constructed. Based on the LSTA-SMO with a linear term of observation error, a sliding mode coefficient can be adjusted in real time according to the change in rotational speed. In view of the high harmonic content of the output back electromotive force, the adaptive adjustment strategy for the back electromotive force is adopted. In addition, in order to improve the estimation accuracy and resistance ability of the observer, the rotor position error was taken as the disturbance term, and the third-order extended state observer (ESO) was constructed to estimate the rotational speed and rotor position through the motor mechanical motion equation. The proposed method is validated in Matlab and compared with the conventional linear super twisted observer. The simulation results show that the proposed method enables the observer to operate stably in a wide velocity domain and reduces the velocity estimation error to 6.7 rpm and the position estimation accuracy error to 0.0005 rad at high speeds, which improves the anti-interference capability. Full article

(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)

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20 pages, 1758 KB

Open AccessArticle

Research on the Identification Method of Respiratory Characteristic Parameters during Mechanical Ventilation

by Yuxin Zhang, Jing Bai, Xingyi Ma and Yu Xu

Processes 2024, 12(8), 1719; https://doi.org/10.3390/pr12081719 - 15 Aug 2024

Viewed by 883

Abstract

In order to enhance the accuracy of ventilator parameter setting, this paper analyzes two identification methods for respiratory characteristic parameters of non-invasive ventilators and invasive ventilators. For non-invasive ventilators, a respiratory characteristic parameter identification method based on a respiration model is established. In this method, the patient’s respiratory sample set is obtained through non-invasive measurements. Experimental results demonstrate that the mean relative error of pulmonary elastance identification was 14.25%, and the mean relative error of intrapulmonary pressure identification was 12.33% using the Romberg integral algorithm. For chronic patients using non-invasive ventilators, the fault-tolerant space for ventilator parameter setting is large; this method meets the requirement of auxiliary setting of non-invasive ventilator parameters. For invasive ventilators, a respiratory characteristic parameter identification method based on the AVOV–BP neural network is established. In this method, the patient’s respiratory sample set is obtained through real-time invasive measurements. Even with small sample datasets, experimental results show that the mean relative error of pulmonary elastance identification and intrapulmonary pressure identification were both 0.22%. For critically ill patients using invasive ventilators, the fault-tolerant space for ventilator parameter setting is small; this method meets the requirement of auxiliary setting of invasive ventilator parameters. Full article

(This article belongs to the Special Issue Design and Analysis of Adaptive Identification and Control)

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